Toward a predictable bioproduction of lipids – Omics driven acceleration of DBTL cycle

Toward a more Predictable and Precision Lipid Production Platform - via Integrative Sciences and Systems Biology

Traditional synthetic biology and metabolic engineering relies heavily on the screening of KPIs – as product titer, productivity and yield are the most accurate and direct read-out (KPI) to design further improvements. Although significant progress has been made to enable key industrial bioderived products, without further understanding of important parameters such as pathway intermediates, microbial physiology and strain robustness, the strain/bioprocess improvements are still a black box.

Recent development in Systems Biology – especially a combination of omics (metabolomics. lipidomics and proteomics) and modeling, has enabled deeper understandings in pathway design, microbial physiology, and cell robustness. In industrial settings, similar learnings have also been applied into different scales to enable successful tech transfer and scale up. Such quantitative studies of microbial pathways and behavior have enabled successful bioengineering of previously termed impossible targets and acceleration of DBTL cycle (Design-Build-Test-Learn) for KPI gains. In this talk, we will cover how systems biology drive to build a more productive and robust lipid bioproduction process. We will also further describe a fit-for-future bioprocess platform - by combining metabolic modeling, fermentation modeling and AI - to enable more predictable and precision lipid bioproduction.